scholarly journals A Hybrid Maximum Power Point Tracking Approach for Photovoltaic Systems under Partial Shading Conditions Using a Modified Genetic Algorithm and the Firefly Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yu-Pei Huang ◽  
Xiang Chen ◽  
Cheng-En Ye
Keyword(s):  
Genetic Algorithm ◽  
Firefly Algorithm ◽  
High Accuracy ◽  
Photovoltaic Systems ◽  
Tracking Accuracy ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

This paper proposes a modified maximum power point tracking (MPPT) algorithm for photovoltaic systems under rapidly changing partial shading conditions (PSCs). The proposed algorithm integrates a genetic algorithm (GA) and the firefly algorithm (FA) and further improves its calculation process via a differential evolution (DE) algorithm. The conventional GA is not advisable for MPPT because of its complicated calculations and low accuracy under PSCs. In this study, we simplified the GA calculations with the integration of the DE mutation process and FA attractive process. Results from both the simulation and evaluation verify that the proposed algorithm provides rapid response time and high accuracy due to the simplified processing. For instance, evaluation results demonstrate that when compared to the conventional GA, the execution time and tracking accuracy of the proposed algorithm can be, respectively, improved around 69.4% and 4.16%. In addition, in comparison to FA, the tracking speed and tracking accuracy of the proposed algorithm can be improved around 42.9% and 1.85%, respectively. Consequently, the major improvement of the proposed method when evaluated against the conventional GA and FA is tracking speed. Moreover, this research provides a framework to integrate multiple nature-inspired algorithms for MPPT. Furthermore, the proposed method is adaptable to different types of solar panels and different system formats with specifically designed equations, the advantages of which are rapid tracking speed with high accuracy under PSCs.

scholarly journals Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm

2021 ◽  
Vol 13 (5) ◽  
pp. 2656
Author(s):  
Ahmed G. Abo-Khalil ◽  
Walied Alharbi ◽  
Abdel-Rahman Al-Qawasmi ◽  
Mohammad Alobaid ◽  
Ibrahim M. Alarifi
Keyword(s):  
Firefly Algorithm ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Opposition Based Learning ◽  
Power Point Tracking ◽  
Convergence Process ◽  
Power Point

This work presents an alternative to the conventional photovoltaic maximum power point tracking (MPPT) methods, by using an opposition-based learning firefly algorithm (OFA) that improves the performance of the Photovoltaic (PV) system both in the uniform irradiance changes and in partial shading conditions. The firefly algorithm is based on fireflies’ search for food, according to which individuals emit progressively more intense glows as they approach the objective, attracting the other fireflies. Therefore, the simulation of this behavior can be conducted by solving the objective function that is directly proportional to the distance from the desired result. To implement this algorithm in case of partial shading conditions, it was necessary to adjust the Firefly Algorithm (FA) parameters to fit the MPPT application. These parameters have been extensively tested, converging satisfactorily and guaranteeing to extract the global maximum power point (GMPP) in the cases of normal and partial shading conditions analyzed. The precise adjustment of the coefficients was made possible by visualizing the movement of the particles during the convergence process, while opposition-based learning (OBL) was used with FA to accelerate the convergence process by allowing the particle to move in the opposite direction. The proposed algorithm was simulated in the closest possible way to authentic operating conditions, and variable irradiance and partial shading conditions were implemented experimentally for a 60 [W] PV system. A two-stage PV grid-connected system was designed and deployed to validate the proposed algorithm. In addition, a comparison between the performance of the Perturbation and Observation (P&O) method and the proposed method was carried out to prove the effectiveness of this method over the conventional methods in tracking the GMPP.

scholarly journals A Modified Firefly Algorithm with Rapid Response Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2284 ◽  
Author(s):  
Yu-Pei Huang ◽  
Cheng-En Ye ◽  
Xiang Chen
Keyword(s):  
Firefly Algorithm ◽  
Rapid Response ◽  
Maximum Power ◽  
Search Process ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
Modified Firefly Algorithm

A rapid response optimization technique for photovoltaic maximum power point tracking (MPPT) under partial shading conditions (PSCs) is proposed in this study. To improve the solar MPPT tracking speed for rapidly-changing environmental conditions and to prevent the conventional firefly algorithm (FA) from becoming trapped at the local peaks and oscillations during the search process, a novel fusion algorithm, named the modified firefly algorithm (MFA), is proposed. The MFA integrates and modifies the processes of two algorithms, namely the firefly algorithm with neighborhood attraction (NaFA) and simplified firefly algorithm (SFA). A modified attraction process for the NaFA is used in the first iteration to avoid trapping at local maximum power points (LMPPs). In addition, in order to improve the convergence speed, the attractiveness factor of the attraction process is designed to be related to the power and position difference of the fireflies. Furthermore, the number of fireflies is designed to decrease in proportion with the iterations in the modified SFA process. Results from both the simulations and evaluations verify that the proposed algorithm offers rapid response with high accuracy and efficiency when encountering PSCs. In addition, the MFA can avoid becoming trapped at LMPPs and ease the oscillations during the search process. Consequently, the proposed method could be considered to be one of the most promising substitutes for existing approaches. In addition, the proposed method is adaptable to different types of solar panels and different system formats with specifically designed parameters.

scholarly journals Maximum Power Point Tracking for Photovoltaic Systems under Partial Shading Conditions Using Bat Algorithm

2018 ◽  
Vol 10 (5) ◽  
pp. 1347 ◽  
Author(s):  
Mehdi Seyedmahmoudian ◽  
Tey Kok Soon ◽  
Elmira Jamei ◽  
Gokul Thirunavukkarasu ◽  
Ben Horan ◽  
...  
Keyword(s):  
Maximum Power Point Tracking ◽  
Bat Algorithm ◽  
Maximum Power ◽  
Photovoltaic Systems ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

scholarly journals An Immune Firefly Algorithm for Tracking the Maximum Power Point of PV Array under Partial Shading Conditions

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3083 ◽  
Author(s):  
Mingrui Zhang ◽  
Zheyang Chen ◽  
Li Wei
Keyword(s):  
Firefly Algorithm ◽  
Maximum Power ◽  
Convergence Time ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Steady State Oscillation ◽  
Power Point

Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.

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